Learning to Rank for Information Retrieval
Date Issued
2009
Date
2009
Author(s)
Tsai, Ming-Feng
Abstract
Learning to rank is becoming important in many fields, especially in information retrieval. In this thesis, a novel learning-based ranking algorithm, Fidelity Rank (FRank), is first proposed to learn an effective ranking function. FRank not only inherits the useful properties of the probabilistic ranking framework, but also possesses new properties helpful for ranking, including slow-growing loss and the ability to reach zero for each document pair. The results demonstrate that FRank outperforms other ranking algorithms for conventional IR problem as well as Web-based searching.hen, we apply the FRank algorithm to enhance the merge quality in multilingual information retrieval (MLIR). To the best of our knowledge, this practice is the first attempt to use a learning-based ranking algorithm to construct a merge model for MLIR merging. The experimental results show that the merge model constructed by FRank can significantly improve merging quality. In addition to the effectiveness, via the merge model, we can further identify key factors that influence the merging process; this information might provide us more insight and understanding into MLIR merging.inally, we investigate how to extend learning-based ranking techniques with more desirable property -- diversity. For ambiguous queries, if there is no further information about user''s intention, an IR system should better provide a ranking list of documents with all possible interpretations. For this diversification problem, we propose a two-step Ranking SVM technique, in which the support vector classification and regression techniques are utilized accordingly to enhance the diversity while maintain the ranking quality. According to the experimental results, the two-step learning technique not only improves ranking quality, but also broaden the coverage within the retrieved results.
Subjects
Learning to Rank
Information Retrieval
Machine Learning
Type
thesis
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